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A number of multi-objective genetic algorithms (MOGAs) have been developed to obtain Pareto optimal solutions for multi-objective optimization problems. However, as these methods involve probabilistic algorithms, there is no guarantee that the global search will be conducted in the design variable space. In such cases, there are unsearched areas in the design variable space, and the obtained Pareto solutions may not be truly optimal. In this paper, we propose an optimization method calleddoi:10.1109/cec.2008.4631125 dblp:conf/cec/WangIHM08 fatcat:tm23ogkz5nfjhhogn3iinuiccm